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Course content

The program

Final exercise

• Choose your own dataset
• Organize your data folder
  
• Read data into R environment using an r script
  
• Select 4 variables (at least one numerical and one categorical)
• Find out what classes your variables have and correct them to the most sensible class if necessary
• Check for missing values for all variables
• Give summary statistics
• For vector/categorical variables give table with count and percentages
• For numerical variables give a table with mean/median, sd, min, max
• Visualize
  
• For numerical: histogram/boxplot
• For categorical: bar

Grading: Pass or fail (0.5 ECT)

Why learn R?

• Reproducibility of your results
• Free software (unlike stata or SPSS)
• A lot of resources available
• Potentially useful in further career

Installing R and RStudio

Did you all manage to install R and RStudio?

Difference R and RStudio

Opening RStudio

Simple calculator

1 + 1 

[1] 2

Using a script

Saving “objects”

a = 1 
b = 2
c = a + b

# can use = or "<-"

c <- a + b 

c

[1] 3

Vector

c(1,2,3)

[1] 1 2 3

c(a, b)

[1] 1 2

Vector

How to access an element in the vector

a_vector = c(1.23, 2.34, 6.21, 3.11, 3.412, 4.32, 5.922, 5.65)

a_vector[4]

[1] 3.11

Array/matrix

matrix(data = c(1,2,3,4,5,6,7,8), nrow = 4 )

     [,1] [,2]
[1,]    1    5
[2,]    2    6
[3,]    3    7
[4,]    4    8

Array/matrix

How to access an element from a matrix

a_matrix = matrix(data = c(1,2,3,4,5,6,7,8), nrow = 4 )

a_matrix[3,2] # first row, then column

[1] 7

Using “functions”

sum(1, 2)

[1] 3

sum(a , b)

[1] 3

?sum

Getting help with functions

?sum

Let`s try!

Can you calculate the following for “a_vector”?

• Mean
• Standard deviation
• Maximal value
• Minimal value
• Length of the vector

a_vector = c(1.23, 2.34, 6.21, 3.11, 3.412, 4.32, 5.922, 5.65)

Let`s try! - Solution

a_vector = c(1.23, 2.34, 6.21, 3.11, 3.412, 4.32, 5.922, 5.65)

mean(a_vector)

[1] 4.02425

sd(a_vector)

[1] 1.811263

max(a_vector)

[1] 6.21

min(a_vector)

[1] 1.23

length(a_vector)

[1] 8

Take 15 minutes to try

Classes and types

Numeric and character

a_vector = c(1.23, 2.34, 6.21, 3.11, 3.412, 4.32, 5.922, 5.65)

class(a_vector)

[1] "numeric"

b_vector = c("something", "something else", "another thing", "completely differnt")

class(b_vector)

[1] "character"

Classes and types

Logical

c_vector = c(F, T, T,T, F, T, F, T)

class(c_vector)

[1] "logical"

Dataframe

# Create a data frame
df <- data.frame(x = 1:3, y = c("a", "b", "c"))

# Printing
print(df)

  x y
1 1 a
2 2 b
3 3 c

Tibble

# Create a tibble
library(tibble)
tb <- tibble(x = 1:3, y = c("a", "b", "c"))

print(tb)

# A tibble: 3 × 2
      x y    
  <int> <chr>
1     1 a    
2     2 b    
3     3 c    

Packages

Get access to specific set of functions

#install.packages("tidyverse")
library(tidyverse)

run “library()” every time you want to use any function from this package

Let’s try!

Organize your data

project_dir

Organize your data

Choose your working directory

getwd() 

[1] "/Users/jb22m516/Documents/GitHub/getting_started_with_R/slides"

setwd("/Users/jb22m516/Documents/GitHub/getting_started_with_R/")

“Path” to file in R

A “path” tells R where it can find your data

Functions for loading data

#install.packages("utils")
library(utils)

#data = read.csv2()

Types of data in R

tibble / dataframe / matrix / array